At present, devices and methods for waypoint target generation and mission spooling for mobile ground robots or Unmanned Ground Vehicle (UGV) require an operator to manually enter waypoints. In order to implement automated waypoint generation, methods of image registration, parallax compensation and change detection would be required. However, it is well known that image registration may not be perfect. In addition, parallax is an important practical issue during data collection. Hence, a robust change detection algorithm such as CKRX is needed.
Support Vector Machines (SVM) and non-deep neural networks (NN) have been used in many pattern classification applications. However, it is believed that there is a lot of room for further improvement. This is because SVM and non-deep NN have only one or two layers of tunable parameters. Since pattern recognition and concentration estimation are complex and involve sophisticated features, SVM and non-deep NN may be restricted in achieving high classification rate.